| Name : R-meerva
| |
| Version : 0.2.2
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.9.2
| Date : 2024-06-14 12:00:56
|
| Group : Development/Libraries/Other
| Source RPM : R-meerva-0.2.2-lp153.9.2.src.rpm
|
| Size : 0.34 MB
| |
| Packager : (none)
| |
| Summary : Analysis of Data with Measurement Error Using a Validation Subsample
|
Description :
Sometimes data for analysis are obtained using more convenient or less
expensive means yielding \"surrogate\" variables for what could be
obtained more accurately, albeit with less convenience; or less
conveniently or at more expense yielding \"reference\" variables, thought
of as being measured without error. Analysis of the surrogate variables
measured with error generally yields biased estimates when the
objective is to make inference about the reference variables. Often it
is thought that ignoring the measurement error in surrogate variables
only biases effects toward the null hypothesis, but this need not be
the case. Measurement errors may bias parameter estimates either toward
or away from the null hypothesis. If one has a data set with surrogate
variable data from the full sample, and also reference variable data
from a randomly selected subsample, then one can assess the bias
introduced by measurement error in parameter estimation, and use this
information to derive improved estimates based upon all available data.
Formulaically these estimates based upon the reference variables from
the validation subsample combined with the surrogate variables from the
whole sample can be interpreted as starting with the estimate from
reference variables in the validation subsample, and \"augmenting\" this
with additional information from the surrogate variables. This suggests
the term \"augmented\" estimate. The meerva package calculates these
augmented estimates in the regression setting when there is a randomly
selected subsample with both surrogate and reference variables.
Measurement errors may be differential or non-differential, in any or
all predictors (simultaneously) as well as outcome. The augmented
estimates derive, in part, from the multivariate correlation between
regression model parameter estimates from the reference variables and
the surrogate variables, both from the validation subset. Because the
validation subsample is chosen at random any biases imposed by
measurement error, whether non-differential or differential, are
reflected in this correlation and these correlations can be used to
derive estimates for the reference variables using data from the whole
sample. The main functions in the package are meerva.fit which
calculates estimates for a dataset, and meerva.sim.block which
simulates multiple datasets as described by the user, and analyzes
these datasets, storing the regression coefficient estimates for
inspection. The augmented estimates, as well as how measurement error
may arise in practice, is described in more detail by Kremers WK (2021)
< arXiv:2106.14063> and is an extension of the works by Chen Y-H, Chen
H. (2000) < doi:10.1111/1467-9868.00243>, Chen Y-H. (2002)
< doi:10.1111/1467-9868.00324>, Wang X, Wang Q (2015)
< doi:10.1016/j.jmva.2015.05.017> and Tong J, Huang J, Chubak J, et al.
(2020) < doi:10.1093/jamia/ocz180>.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
